prunePed: Prunes a pedigree based on individuals with phenotypes

Description

This function removes individuals who are either not themselves or not ancestors to phenotyped individuals

Usage

prunePed(pedigree, phenotyped, ...)
# S3 method for default
prunePed(pedigree, phenotyped, ...)
# S3 method for numPed
prunePed(pedigree, phenotyped, ...)

Value

The pedigree object (can have more columns than just ID, Dam, and Sire), where the ID column contains an ID for all individuals who are actually phenotyped or are an ancestor to an individual with a phenotype (and are thus informative for estimating parameters in the base population).

Arguments

pedigree: An object, where the first 3 columns correspond to: ID, Dam, & Sire. See details.
phenotyped: A vector indicating which individuals in the pedigree have phenotypic information available.
...: Arguments to be passed to methods

Details

Often mixed effect models run much faster when extraneous information is removed before running the model. This is particularly so when reducing the number of random effects associated with a relationship matrix constructed from a pedigree.

NOTE: more columns than just a pedigree can be passed in the pedigree argument.

Missing parents (e.g., base population) should be denoted by either 'NA', '0', or '*'.

This function is very similar to (and the code is heavily borrowed from) a function of the same name in the MCMCglmm package by Jarrod Hadfield.

Examples

Run this code



# Make a pedigree (with sex) from the warcolak dataset
  warcolak_ped <- warcolak[, 1:4]

# Reduce the number of individuals that have a phenotype for "trait1" in
  #the warcolak dataset
  t1phenotyped <- warcolak
  t1phenotyped[sample(seq.int(nrow(warcolak)), 1500, replace = FALSE), "trait1"] <- NA
  t1phenotyped <- t1phenotyped[which(!is.na(t1phenotyped$trait1)), ]

# The following will give a pedigree with only individuals that have a 
# phenotype for "trait1" OR are an ancestor to a phenotyped individual.
  pruned_warcolak_ped <- prunePed(warcolak_ped, phenotyped = t1phenotyped$ID)

# Now compare the sizes (note, pruned_warcolak_ped retained its column indicating sex.
  dim(warcolak_ped)
  dim(pruned_warcolak_ped)
# We could have kept all of the data associated with individuals who had phenotypic
# information on "trait1" by instead specifying 
  pruned_fullt1_warcolak_ped <- prunePed(warcolak, phenotyped = t1phenotyped$ID) 
  dim(pruned_fullt1_warcolak_ped)  #<-- compare number of columns with above